Intrusion Detection on Industrial Networks Using Neuromorphic Computing

Cyber-attacks on Industrial Control Systems (ICS) present critical risks to operational stability, public safety, and national security. As industrial networks become more integrated and interconnected, their susceptibility to sophisticated cyber threats increases. Traditional security measures often fall short in detecting evolving attack patterns. This paper investigates the application of neuromorphic computing, which emulates the human brain's pattern recognition capabilities, as an innovative approach to intrusion detection in industrial networks. We introduce a hardware-based Spiking Neural Network (SNN) engineered to monitor and analyze communication packets in real time, detecting anomalies indicative of potential intrusions. The neuromorphic approach offers a promising solution to the growing challenge of cyber-attacks in critical infrastructure. The proposed system is implemented on an FPGA, focusing initially on monitoring key packet types such as LLDP, ARP, UDP, TCP, and Profibus RT, with scalability to accommodate additional protocols. The results demonstrate the efficacy of this SNN-based intrusion detection system in providing real-time, efficient, and accurate monitoring of industrial networks.